A predictive nomogram for short‐term outcomes of myasthenia gravis patients treated with low‐dose rituximab

Abstract Background This study aims to establish and validate a predictive nomogram for the short‐term clinical outcomes of myasthenia gravis (MG) patients treated with low‐dose rituximab. Methods We retrospectively reviewed 108 patients who received rituximab of 600 mg every 6 months in Huashan Hospital and Tangdu Hospital. Of them, 76 patients from Huashan Hospital were included in the derivation cohort to develop the predictive nomogram, which was externally validated using 32 patients from Tangdu Hospital. The clinical response is defined as a ≥ 3 points decrease in QMG score within 6 months. Both clinical and genetic characteristics were included to screen predictors via multivariate logistic regression. Discrimination and calibration were measured by the area under the receiver operating characteristic curve (AUC‐ROC) and Hosmer–Lemeshow test, respectively. Results Disease duration (OR = 0.987, p = 0.032), positive anti‐muscle‐specific tyrosine kinase antibodies (OR = 19.8, p = 0.007), and genotypes in FCGR2A rs1801274 (AG: OR = 0.131, p = 0.024;GG:OR = 0.037, p = 0.010) were independently associated with clinical response of post‐rituximab patients. The nomogram identified MG patients with clinical response with an AUC‐ROC (95% CI) of 0.875 (0.798–0.952) in the derivation cohort and 0.741(0.501–0.982) in the validation cohort. Hosmer–Lemeshow test showed a good calibration (derivation: Chi‐square = 3.181, p = 0.923; validation: Chi‐square = 8.098, p = 0.424). Conclusions The nomogram achieved an optimal prediction of short‐term outcomes in patients treated with low‐dose rituximab.


| INTRODUC TI ON
Myasthenia gravis (MG) is an autoimmune disorder characterized by autoantibodies against neuromuscular junctions, including anti-acetylcholine receptor (AChR) and anti-muscle-specific tyrosine kinase (MuSK) antibodies.Approximately 10%-15% of MG patients with anti-AChR antibody-positive (AChR-MG) and most anti-MuSK antibody-positive MG (MuSK-MG) patients do not respond to standard treatments, such as glucocorticoids and immunosuppressants (IS) like azathioprine, tacrolimus, or mycophenolate mofetil.2][3][4][5] As a result, novel biological targeting agents, such as rituximab (RTX), are increasingly being used in the treatment of MG.
RTX is a monoclonal IgG1 antibody that targets the B lymphocyte membrane protein CD20.There are still controversies regarding the target population and optimal regimen for MG treatment.
Our previous studies have shown that low-dose RTX (500-600 mg, every 6 months) is effective in treating AChR-MG and MuSK-MG patients. 6,7However, the response to treatment varied among individuals, and five of 12 AChR-MG patients showed less than 3 points reduction in the quantitative myasthenia gravis (QMG) score 6 months after the first RTX infusion. 8 to now, predictors for treatment response to RTX in MG have not been investigated.In rheumatoid arthritis (RA), 9 systemic vasculitis, 10 neuromyelitis optic spectrum disorder (NMOSD), 11 and systemic lupus erythematosus(SLE), 12 both clinical parameters (such as disease severity, age at onset, age at RTX start, and gender) and biomarkers (including the titer of antibodies, the frequency of memory B cells, single-nucleotide polymorphism(SNP) in BAFF, FCGR3A, FCGR2A, and IL-12/21) have been shown to be related to treatment response.Since there is wide heterogenicity in the treatment efficacy among different patients, it is essential to identify predictors related to treatment response in MG.
In this study, we developed and validated a nomogram that predicts the clinical response of RTX in AChR-MG and MuSK-MG patients based on their baseline clinical characteristics and SNPs.(4) Limb score: the ability to brush teeth or comb hair, and arise from a chair.

| Outcome assessment
The clinical response is defined as a ≥ 3 points decrease in QMG score within 6 months.

| Statistical analysis
All continuous variables have undergone the Shapiro-Wilk test for normality.Continuous variables that followed a normal distribution are reported as the mean ± standard deviation.Non-normally distributed data are presented as the median (interquartile range, IQR).

Categorical variables are expressed as frequencies (percentages).
The comparison between cohorts was performed using chi-squared or Fisher's exact test for categorical variables and the Student's t-

| Model derivation
The model was derived through the following steps: The significance of each variable in the derivation cohort was analyzed using univariate logistic regression analysis.The variance inflation factors

| Model validation
The discrimination performance of the nomogram was measured using the area under the receiver operating characteristic curve

| Clinical characteristics
A total of 163 MG patients who were treated with 600 mg rituximab every 6 months were initially registered in two tertiary referral centers.Following the inclusion and exclusion flowchart, we finally enrolled 108 MG patients in the baseline registry.Of them, 26 AChR-MG and 6 MuSK-MG patients from Tangdu Hospital were enrolled for external validation (Figure 1).
Clinical and demographic profiles as well as SNPs, of the derivation and external validation cohorts are outlined and compared in Table 1.The frequency of patients achieving clinical response was similar for the derivation (76%) and external validation cohorts (88%), whereas there were some differences between the two cohorts regarding age at onset, age at the first RTX treatment, disease duration, antibody subtype, MGFA classification, comorbid autoimmune diseases, history of PE, IVIg, and thymectomy, ADL-bulbar score, ADL-limb score, and QMG score.
We then selected these three predictors to build a nomogram, which can calculate the total point for each post-RTX MG patient and convert it to predicted probabilities of response (Figure 2).
The model exhibited high discriminatory power with an AUC of 0.875 (95% CI: 0.798-0.952,Figure 3A) in the derivation cohort.
The observed percentages of response corresponded well with the predicted possibilities (Hosmer-Lemeshow goodness-of-fit test: chisquare = 3.181, p = 0.923, Figure 4A).Similar discriminatory power was exhibited in internal validation using resampling data (mean AUC = 0.889, 95% CI: 0.851-0.929,Figure 3B).On the calibration TA B L E 1 The baseline demographic and clinical characteristics of the derivation and external validation cohort.curve, the model's predicted probabilities were close to the observed probabilities (Figure 4B).The nomogram also performed well in the external validation cohort with good discrimination and calibration (AUC = 0.741, 95% CI: 0.501-0.982,Figure 3C; Hosmer-Lemeshow goodness-of-fit: Chi-square = 8.098, p = 0.424, Figure 4C).

| DISCUSS ION
The effectiveness of RTX in treating MG has been confirmed in both randomized double-blind controlled trials 15 and real-world studies. 16,17However, not all patients in these studies achieved significant improvement.For both clinicians and patients, it is crucial to predict whether the effects of rituximab are clinically significant prior to making treatment-related decisions.In our multicenter study, we identified that a shorter disease duration, a positive anti-MuSK antibody, and the AA genotype in FCGR2A rs1801274 were significant predictors for achieving a clinical response within 6 months in MG patients treated with rituximab of 600 mg.Our predictive model might provide valuable information for the optimal use of rituximab in a real-world setting by integrating these predictors into clinical practice.
We observed that a shorter duration from onset to rituximab exposure was a favorable predictor for clinical response.A prospective study also found that the clinical outcomes of rituximab were more favorable in new-onset generalized myasthenia gravis compared with the refractory group. 18Consistent with our study, in the RINOMAX Randomized Clinical Trial, 15 MG patients who experienced generalized symptoms within 12 months of onset showed a higher probability of minimal MG manifestations after receiving a single dose of 500 mg of rituximab.From a pathophysiological perspective, early initiation of effective immunotherapy appears to be more beneficial in achieving a clinical response by preventing structural damage to the neuromuscular junction.From an immunological standpoint, early immune responses are primarily mediated by plasmablasts and short-lived plasma cells. 191][22][23][24][25] It is plausible to speculate that early administration of RTX treatment by depleting immature, mature B cells, memory B cells, and plasmablasts may restrict the establishment of pathogenic antibody-produced plasma cell pools. 26mpared with AChR-MG, MuSK-MG exhibits advantages in RTX therapy, as demonstrated in numerous clinical studies. 27,28ese differences may be attributed to the distinct pathogenesis of the antibodies: (1) Anti-MuSK antibodies are primarily produced by short-lived plasmablasts, whereas anti-AChR antibodies are generated by long-lived plasma cells and memory B cells. 29,30Our previous studies have confirmed that low-dose RTX can successfully delete B lymphocytes and reduce serum levels of pathogenic antibodies. 7The superior response of MuSK-MG patients may be related to the shorter lifespan and faster exhaustion of pathogenic antibody-producing cells (such as short-lived plasmablasts).(2)   Anti-MuSK antibodies are predominantly of the IgG4 subtype, which generally does not activate complement, while anti-AChR antibodies are of the IgG1 and IgG3 subtypes, which can amplify the damage by activating the complement cascade. 29,30eatment with anti-CD20 antibodies cannot directly prevent the complement-mediated membrane lysis and postsynaptic damage.
(3) By binding to the postsynaptic membrane receptor, anti-AChR antibodies can not only block signal transmission but also lead to endocytosis and therefore a loss of AChR densities.In contrast, anti-MuSK antibodies predominantly disrupt the signaling pathways and impair the clustering of AChR receptors.The process of structural repair may require more time compared to the adjustment of functional dysfunction.(4) Anti-AChR antibodies can interfere with the muscle regeneration process by regulating myogenic markers, while the effect of anti-MuSK antibodies on regeneration remains unclear. 31udies have also demonstrated that genetic factors, specifically SNPs, are associated with interindividual variations in response to RTX among patients with autoimmune diseases.For instance, Caucasian rheumatoid arthritis (RA) patients with the AA genotype in FCGR2A rs1801274 exhibited a higher remission rate 6 months after RTX treatment. 14Similarly, RA patients displayed a better response when possessing the FCGR3A rs396991-V158 allele. 32,33nsistent with previous researches, our study also identified that FCGR2A rs1801274-G was an unfavorable factor for RTX response in an additive model.However, we did not observe the association of FCGR3A rs396991-A with the clinical response.5][36] Interestingly, FcγRIIA exhibits a higher affinity for IgG1 than that of FcγRIIIA. 37Polymorphisms within the genetic coding regions of these two receptors could also affect their affinity with the Fc fragment of rituximab, thereby showing an impact on depleting B cells. 38,391][42] Similarly, the V158 variant in FCGR3A (rs396991-C) demonstrated a greater affinity for IgG1. 43,44Therefore, polymorphisms in Fcγ receptors could potentially impact the therapeutic efficacy of RTX by either augmenting or diminishing ADCC or ADCP mediated by Fcγ receptors carried by effector cells.
Our study had several limitations: First, it was a retrospective study with a relatively small sample size.Second, incomplete medical records and unavoidable missing data led to the exclusion of certain variables, including the initial dosage of steroids and the frequency of exacerbations before RTX treatment.Third, since there was heterogeneity in response between the external and the internal cohort, the findings from this study require further This retrospective observational study included MG patients from the registry database of Huashan Hospital, Fudan University, and Tangdu Hospital, the Fourth Military Medical University, from July 2015 to April 2023.Of 1828 generalized MG patients registered at Huashan Hospital, 7.06% (129) have received rituximab treatment; in the database of 451 patients with generalized MG from Tangdu Hospital, 7.54% (34) were treated with rituximab.Inclusion criteria were as follows: (1) age ≥16 years old; (2) anti-MuSK or anti-AChR antibody positive; (3) QMG scores ≥3; (4) treated with 600 mg rituximab; and (5) duration from rituximab exposure to last visit was at least 6 months.Patients with incomplete clinical data at baseline were excluded.Eligible patients from Huashan Hospital with integrated data were included in the derivation cohort and patients from Tangdu Hospital were included in the validation cohort.Written informed consent was granted by each patient and the study was approved by the Institutional Review Board of Huashan Hospital, Fudan University, and Tangdu Hospital, the Fourth Military Medical University.
test or Mann-Whitney U test for continuous variables.Data analysis was carried out using IBM SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) and R version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria).All diagrams were generated in R version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria).Statistical significance was defined as a two-tailed p < 0.05.

(
VIFs) were generated to examine individual predictors for potential contributions to multicollinearity.Variables that showed statistical (p < 0.05) and clinical significance in the univariate analysis were included in the backward multivariate logistic regression model to select independent predictors of clinical response (p < 0.05) and develop the nomogram.

(
AUC-ROC) in both the derivation and validation cohorts, with 95% confidence intervals (95% CI) provided.Calibration was evaluated using the Hosmer-Lemeshow goodness-of-fit test and calibration plots.A two-tailed p < 0.05 was considered significant.Model validation was conducted in two steps.First, internal validation was performed using a bootstrap resampling process to provide an unbiased estimate of model performance.Second, external validation was conducted by assessing the prediction accuracy of the clinical response nomogram on the validation cohort through the computation of AUC-ROC and calibration plots.

F I G U R E 2 | 7 of 9 ZHOU
Nomogram to estimate the probability of clinical response in MG patients after low-dose rituximab treatment.To use this nomogram, locate the position of each variable on the corresponding axis, draw a line from that point to the point axis to represent the number of points, add up the points from all of the variables, and draw a line from that total point value to the probability axis to determine the probability of clinical response at the lower line of the nomogram (as indicated the red marker).I G U R E 3 ROC-AUC of nomogram.(A) Derivation cohort: ROC-AUC = 0.875, 95% CI: 0.798-0.952.(B) Resampling: mean ROC-AUC = 0.889, 95% CI: 0.851-0.929.(C) Validation cohort: ROC-AUC = 0.741, 95% CI: 0.501-0.982.ROC-AUC, Area under the receiver operating characteristic curve.F I U R E 4 Model calibration of derivation cohort (A), resampling data using bootstrap (B), and validation cohort (C).The x-and y-axes in the graph represent the predicted and actual response probabilities from the nomogram, respectively.The 45° gray line serves as the reference line, indicating perfect calibration power.et al.
Demographic variables in this study include gender, age at onset, and age at first RTX treatment.Clinical features before the first RTX treatment, include the comorbidities of other autoimmune Univariate and multivariate logistic regression analysis for clinical response in the derivation cohort.